# Visualisation 1 World Map
# Clean data for visualization one.
vis1 <- dplyr::select(indicator, country, obs_value)
# Obtain average for each country.
vis1 <- dplyr::reframe(vis1, .by = country, "Labour Force Participation Rate" = mean(.data[["obs_value"]], na.rm = TRUE))
# Get longitude and latitude for each country.
coords <- map_data("world")
# Remove unnecessary columns.
coords <- dplyr::select(coords, -order, -subregion)
# Correct mismatching country names.
coords$region[coords$region == "USA"] <- "United States"
coords$region[coords$region == "Russian Federation"] <- "Russia"
coords$region[coords$region == "Democratic Republic of the Congo"] <- "Congo, the Democratic Republic of the"
coords$region[coords$region == "Republic of Congo"] <- "Congo"
coords$region[coords$region == "Iran"] <- "Iran, Islamic Republic of"
coords$region[coords$region == "Syria"] <- "Syrian Arab Republic"
coords$region[coords$region == "Tanzania"] <- "Tanzania, United Republic of"
coords$region[coords$region == "Laos"] <- "Lao People's Democratic Republic"
coords$region[coords$region == "UK"] <- "United Kingdom"
coords$region[coords$region == "Moldova"] <- "Moldova, Republic of"
coords$region[coords$region == "North Macedonia"] <- "Macedonia, the former Yugoslav Republic of"
coords$region[coords$region == "North Korea"] <- "Korea, Democratic People's Republic of"
# Join longitude and latitude co-ordinates with data.
vis1 <- dplyr::full_join(vis1, coords, by = c("country" = "region"))
v1 <- ggplot(vis1) +
aes(x = long, y = lat, group = group, fill = `Labour Force Participation Rate`, text = paste("Country:", country)) +
labs(title="Labour Force Participation Rate by Country", subtitle = "2019") +
theme_void() +
borders() +
scale_fill_viridis(option="viridis", direction = -1) +
theme(plot.title = element_text(size = 12, hjust = 0.5),
plot.subtitle = element_text(size = 10, hjust = 0.5),
plot.caption = element_text(size = 8, hjust = 1)) +
coord_fixed(ratio = 1.205458) +
geom_polygon()
plotly::ggplotly(v1)Visualisations
This project was an exploration of global labour force participation rates and average life expectancy.
Visual 1 unveils a world map shaded by average labour participation rates, revealing Africa as a standout region with notably higher rates compared to the rest of the world. This stark contrast prompts further investigation into the factors influencing this disparity.
# Visualisation 2 Bar Chart
# Clean unicef metadata for visualisation 2.
vis2 <- dplyr::select(metadata, country, year, `Life.expectancy.at.birth..total..years.`)
# Rename the awkward columns.
colnames(vis2) <- c("Country", "Year", "Life Expectancy")
colours <- seq(1:4)
v2 <- vis2 %>%
# We are comparing this data to 2019 labour force participation rates
# therefore we are only interested in 2019 data.
filter(Year == 2019) %>%
arrange(`Life Expectancy`) %>%
# Filter the 35 countries with the lowest life expectancy.
slice(1:35) %>%
ggplot(aes(Country, `Life Expectancy`)) +
geom_bar(stat="identity") +
coord_flip()
plotly::ggplotly(v2)The bar chart, Visual 2, displaying the 35 lowest life expectancies globally offers a concise overview of the regions facing significant health disparities. It shows that every country is below the average world life expectancy of 75.
# Visualisation 3 Scatter Plot
# Combine 2019 labour force participation rates with 2019 life expectancies.
# Get average labour force participation as we did for vis 1.
vis3 <- indicator %>%
select(country, obs_value) %>%
reframe(.by = country, "Labour Force Participation Rate" = mean(.data[["obs_value"]], na.rm = TRUE)) %>%
full_join(metadata %>%
filter(year == 2019) %>%
select(country, `Life.expectancy.at.birth..total..years.`), by = "country") %>%
filter(!is.na(`Life.expectancy.at.birth..total..years.`)) %>%
filter(!is.na(`Labour Force Participation Rate`))
# Rename the awkward columns.
colnames(vis3) <- c("Country", "Labour Force Participation Rate", "Average Life Expectancy")
v3 <- ggplot(vis3, aes(x=`Labour Force Participation Rate`, y=`Average Life Expectancy`)) +
geom_point() +
geom_smooth(method="lm", se=FALSE, color="red")
plotly::ggplotly(v3)`geom_smooth()` using formula = 'y ~ x'Delving deeper into the relationship between labour participation and life expectancy, Visual 3 presents a scatter plot juxtaposing the average observed value of labour participation against life expectancy at birth. Interpreting the linear regression line reveals a non-existent or weak negative correlation between labour force participation rate and average life expectancy.
# Visualisation 4 Time-Series Chart
# Isolate Madagascar data for the time series chart.
vis4 <- metadata %>%
filter(country == "Madagascar") %>%
select(country, year, `Life.expectancy.at.birth..total..years.`)
# Rename the awkward columns.
colnames(vis4) <- c("Country", "Year", "Average Life Expectancy")
v4 <- vis4 %>%
ggplot(aes(Year, `Average Life Expectancy`)) +
labs(title="Average Life Expectancy Madagascar") +
geom_line()
plotly::ggplotly(v4)The upward trend in Madagascar’s life expectancy (highest labour rate in the world), from 43 years in 1960 to 65 years in 2019, may partly stem from its high labour rate. A robust workforce suggests economic activity, which could have translated into improved healthcare infrastructure and access to services.
Conclusion
To summarise, these visualizations provide a nuanced portrait of the intricate dynamics shaping labour participation and life expectancy on a global scale, underscoring the pressing need for targeted interventions to address disparities and promote equitable access to opportunities and healthcare services worldwide, although this is not true for every country.